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Composable-Diffusion / composable_stable_diffusion_pipeline.py

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Fix scheduler unneeded set_format (#9)

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	"""
	modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
	"""
	import inspect
	import warnings
	from typing import List, Optional, Union

	import torch

	from tqdm.auto import tqdm
	from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

	from diffusers.models import AutoencoderKL, UNet2DConditionModel
	from diffusers.pipeline_utils import DiffusionPipeline
	from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
	from safety_checker import StableDiffusionSafetyChecker

	from dataclasses import dataclass
	from typing import List, Union

	import numpy as np

	import PIL

	from diffusers.utils import BaseOutput


	@dataclass
	class StableDiffusionPipelineOutput(BaseOutput):
	"""
	Output class for Stable Diffusion pipelines.
	Args:
	images (`List[PIL.Image.Image]` or `np.ndarray`)
	List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
	num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
	nsfw_content_detected (`List[bool]`)
	List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
	(nsfw) content.
	"""

	images: Union[List[PIL.Image.Image], np.ndarray]
	nsfw_content_detected: List[bool]

	class ComposableStableDiffusionPipeline(DiffusionPipeline):
	r"""
	Pipeline for text-to-image generation using Stable Diffusion.

	This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
	library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

	Args:
	vae ([`AutoencoderKL`]):
	Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
	text_encoder ([`CLIPTextModel`]):
	Frozen text-encoder. Stable Diffusion uses the text portion of
	[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
	the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
	tokenizer (`CLIPTokenizer`):
	Tokenizer of class
	[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
	unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
	scheduler ([`SchedulerMixin`]):
	A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
	[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
	safety_checker ([`StableDiffusionSafetyChecker`]):
	Classification module that estimates whether generated images could be considered offsensive or harmful.
	Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
	feature_extractor ([`CLIPFeatureExtractor`]):
	Model that extracts features from generated images to be used as inputs for the `safety_checker`.
	"""

	def __init__(
	self,
	vae: AutoencoderKL,
	text_encoder: CLIPTextModel,
	tokenizer: CLIPTokenizer,
	unet: UNet2DConditionModel,
	scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
	safety_checker: StableDiffusionSafetyChecker,
	feature_extractor: CLIPFeatureExtractor,
	):
	super().__init__()
	self.register_modules(
	vae=vae,
	text_encoder=text_encoder,
	tokenizer=tokenizer,
	unet=unet,
	scheduler=scheduler,
	safety_checker=safety_checker,
	feature_extractor=feature_extractor,
	)

	def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
	r"""
	Enable sliced attention computation.

	When this option is enabled, the attention module will split the input tensor in slices, to compute attention
	in several steps. This is useful to save some memory in exchange for a small speed decrease.

	Args:
	slice_size (`str` or `int`, optional, defaults to `"auto"`):
	When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
	a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
	`attention_head_dim` must be a multiple of `slice_size`.
	"""
	if slice_size == "auto":
	# half the attention head size is usually a good trade-off between
	# speed and memory
	slice_size = self.unet.config.attention_head_dim // 2
	self.unet.set_attention_slice(slice_size)

	def disable_attention_slicing(self):
	r"""
	Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
	back to computing attention in one step.
	"""
	# set slice_size = `None` to disable `attention slicing`
	self.enable_attention_slicing(None)

	@torch.no_grad()
	def __call__(
	self,
	prompt: Union[str, List[str]],
	height: Optional[int] = 512,
	width: Optional[int] = 512,
	num_inference_steps: Optional[int] = 50,
	guidance_scale: Optional[float] = 7.5,
	eta: Optional[float] = 0.0,
	generator: Optional[torch.Generator] = None,
	latents: Optional[torch.FloatTensor] = None,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	weights: Optional[str] = "",
	**kwargs,
	):
	r"""
	Function invoked when calling the pipeline for generation.

	Args:
	prompt (`str` or `List[str]`):
	The prompt or prompts to guide the image generation.
	height (`int`, optional, defaults to 512):
	The height in pixels of the generated image.
	width (`int`, optional, defaults to 512):
	The width in pixels of the generated image.
	num_inference_steps (`int`, optional, defaults to 50):
	The number of denoising steps. More denoising steps usually lead to a higher quality image at the
	expense of slower inference.
	guidance_scale (`float`, optional, defaults to 7.5):
	Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
	`guidance_scale` is defined as `w` of equation 2. of [Imagen
	Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
	1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
	usually at the expense of lower image quality.
	eta (`float`, optional, defaults to 0.0):
	Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
	[`schedulers.DDIMScheduler`], will be ignored for others.
	generator (`torch.Generator`, optional):
	A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
	deterministic.
	latents (`torch.FloatTensor`, optional):
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will ge generated by sampling using the supplied random `generator`.
	output_type (`str`, optional, defaults to `"pil"`):
	The output format of the generate image. Choose between
	[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
	plain tuple.

	Returns:
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
	When returning a tuple, the first element is a list with the generated images, and the second element is a
	list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
	(nsfw) content, according to the `safety_checker`.
	"""

	if "torch_device" in kwargs:
	device = kwargs.pop("torch_device")
	warnings.warn(
	"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
	" Consider using `pipe.to(torch_device)` instead."
	)

	# Set device as before (to be removed in 0.3.0)
	if device is None:
	device = "cuda" if torch.cuda.is_available() else "cpu"
	self.to(device)

	if isinstance(prompt, str):
	batch_size = 1
	elif isinstance(prompt, list):
	batch_size = len(prompt)
	else:
	raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

	if height % 8 != 0 or width % 8 != 0:
	raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

	if '\|' in prompt:
	prompt = [x.strip() for x in prompt.split('\|')]
	print(f"composing {prompt}...")

	# get prompt text embeddings
	text_input = self.tokenizer(
	prompt,
	padding="max_length",
	max_length=self.tokenizer.model_max_length,
	truncation=True,
	return_tensors="pt",
	)
	text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]

	if not weights:
	# specify weights for prompts (excluding the unconditional score)
	print('using equal weights for all prompts...')
	pos_weights = torch.tensor([1 / (text_embeddings.shape[0] - 1)] * (text_embeddings.shape[0] - 1),
	device=self.device).reshape(-1, 1, 1, 1)
	neg_weights = torch.tensor([1.], device=self.device).reshape(-1, 1, 1, 1)
	mask = torch.tensor([False] + [True] * pos_weights.shape[0], dtype=torch.bool)
	else:
	# set prompt weight for each
	num_prompts = len(prompt) if isinstance(prompt, list) else 1
	weights = [float(w.strip()) for w in weights.split("\|")]
	if len(weights) < num_prompts:
	weights.append(1.)
	weights = torch.tensor(weights, device=self.device)
	assert len(weights) == text_embeddings.shape[0], "weights specified are not equal to the number of prompts"
	pos_weights = []
	neg_weights = []
	mask = [] # first one is unconditional score
	for w in weights:
	if w > 0:
	pos_weights.append(w)
	mask.append(True)
	else:
	neg_weights.append(abs(w))
	mask.append(False)
	# normalize the weights
	pos_weights = torch.tensor(pos_weights, device=self.device).reshape(-1, 1, 1, 1)
	pos_weights = pos_weights / pos_weights.sum()
	neg_weights = torch.tensor(neg_weights, device=self.device).reshape(-1, 1, 1, 1)
	neg_weights = neg_weights / neg_weights.sum()
	mask = torch.tensor(mask, device=self.device, dtype=torch.bool)

	# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
	# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
	# corresponds to doing no classifier free guidance.
	do_classifier_free_guidance = guidance_scale > 1.0
	# get unconditional embeddings for classifier free guidance
	if do_classifier_free_guidance:
	max_length = text_input.input_ids.shape[-1]

	if torch.all(mask):
	# no negative prompts, so we use empty string as the negative prompt
	uncond_input = self.tokenizer(
	[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
	)
	uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]

	# For classifier free guidance, we need to do two forward passes.
	# Here we concatenate the unconditional and text embeddings into a single batch
	# to avoid doing two forward passes
	text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

	# update negative weights
	neg_weights = torch.tensor([1.], device=self.device)
	mask = torch.tensor([False] + mask.detach().tolist(), device=self.device, dtype=torch.bool)

	# get the initial random noise unless the user supplied it

	# Unlike in other pipelines, latents need to be generated in the target device
	# for 1-to-1 results reproducibility with the CompVis implementation.
	# However this currently doesn't work in `mps`.
	latents_device = "cpu" if self.device.type == "mps" else self.device
	latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
	if latents is None:
	latents = torch.randn(
	latents_shape,
	generator=generator,
	device=latents_device,
	)
	else:
	if latents.shape != latents_shape:
	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
	latents = latents.to(self.device)

	# set timesteps
	accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
	extra_set_kwargs = {}
	if accepts_offset:
	extra_set_kwargs["offset"] = 1

	self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)

	# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
	if isinstance(self.scheduler, LMSDiscreteScheduler):
	latents = latents * self.scheduler.sigmas[0]

	# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
	# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
	# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
	# and should be between [0, 1]
	accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
	extra_step_kwargs = {}
	if accepts_eta:
	extra_step_kwargs["eta"] = eta

	for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
	# expand the latents if we are doing classifier free guidance
	latent_model_input = torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents
	if isinstance(self.scheduler, LMSDiscreteScheduler):
	sigma = self.scheduler.sigmas[i]
	# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
	latent_model_input = latent_model_input / ((sigma2 + 1) 0.5)

	# reduce memory by predicting each score sequentially
	noise_preds = []
	# predict the noise residual
	for latent_in, text_embedding_in in zip(
	torch.chunk(latent_model_input, chunks=latent_model_input.shape[0], dim=0),
	torch.chunk(text_embeddings, chunks=text_embeddings.shape[0], dim=0)):
	noise_preds.append(self.unet(latent_in, t, encoder_hidden_states=text_embedding_in).sample)
	noise_preds = torch.cat(noise_preds, dim=0)

	# perform guidance
	if do_classifier_free_guidance:
	noise_pred_uncond = (noise_preds[~mask] * neg_weights).sum(dim=0, keepdims=True)
	noise_pred_text = (noise_preds[mask] * pos_weights).sum(dim=0, keepdims=True)
	noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

	# compute the previous noisy sample x_t -> x_t-1
	if isinstance(self.scheduler, LMSDiscreteScheduler):
	latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
	else:
	latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

	# scale and decode the image latents with vae
	latents = 1 / 0.18215 * latents
	image = self.vae.decode(latents).sample

	image = (image / 2 + 0.5).clamp(0, 1)
	image = image.cpu().permute(0, 2, 3, 1).numpy()

	# run safety checker
	safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
	image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)

	if output_type == "pil":
	image = self.numpy_to_pil(image)

	if not return_dict:
	return (image, has_nsfw_concept)

	return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)